Methods of yield stability analysis in long-term field experiments. A review

Abstract

In the face of a changing climate, yield stability is becoming increasingly important for farmers and breeders. Long-term field experiments (LTEs) generate data sets that allow the quantification of stability for different agronomic treatments. However, there are no commonly accepted guidelines for assessing yield stability in LTEs. The large diversity of options impedes comparability of results and reduces confidence in conclusions. Here, we review and provide guidance for the most commonly encountered methodological issues when analysing yield stability in LTEs. The major points we recommend and discuss in individual sections are the following: researchers should (1) make data quality and methodological approaches in the analysis of yield stability from LTEs as transparent as possible; (2) test for and deal with outliers; (3) investigate and include, if present, potentially confounding factors in the statistical model; (4) explore the need for detrending of yield data; (5) account for temporal autocorrelation if necessary; (6) make explicit choice for the stability measures and consider the correlation between some of the measures; (7) consider and account for dependence of stability measures on the mean yield; (8) explore temporal trends of stability; and (9) report standard errors and statistical inference of stability measures where possible. For these issues, we discuss the pros and cons of the various methodological approaches and provide solutions and examples for illustration. We conclude to make ample use of linking up data sets, and to publish data, so that different approaches can be compared by other authors and, finally, consider the impacts of the choice of methods on the results when interpreting results of yield stability analyses. Consistent use of the suggested guidelines and recommendations may provide a basis for robust analyses of yield stability in LTEs and to subsequently design stable cropping systems that are better adapted to a changing climate.

Grain Yield Stability of Cereal-Legume Intercrops Is Greater Than Sole Crops in More Productive Conditions

“…Adjusted coefficients of variance (aCV) were calculated separately for each location and species combination (barley-pea and wheat-faba bean), according to [22]. Adjustment of the slope b of the TPL log-log regression to a value of 2 was done based on the TPL regression statistics from a data set of cereal and legume crops grown in long-term experiments across Europe [24] similar to the crops and conditions in our data set, because our data were insufficient to generate a robust value of b according to a procedure suggested by [25]. Expected and observed aCV based on the corresponding Y exp and Y obs values from the different locations, plant teams, fertilizer levels and years were computed to obtain overall estimates of stability of grain yields in intercrops vs. sole crops according to the following stability index (SI) equation:…”

Recent Patterns of Exposure, Sensitivity, and Adaptive Capacity of Selected Crops in Cameroon

“…These finding show that different crops are vulnerable at the national and subnational scale. This is important to underscore because what is obtained at the national scale might not be reflected at the sub-national scale as different regions are differentially affected by different precipitation characteristics, temperatures, soils, access to farm inputs, high yielding varieties and crop pests [6,48,59,60]. Therefore, just like with national scale rice, northern maize will benefit from a combination of irrigation and farm inputs in the context of organic fertilizers and enhanced farmer access to climate related crop planting information to better guide farming decisions.…”

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“…Найбільш тривалим польовим дослідам Ротамстедської дослідної станції у Великій Британії виповнилося понад 170 років, дослідам у Франції та США -145, а всього у світі лише 6 країн мають польові досліди, які тривають понад 100 років [1]. Довгострокові польові експерименти генерують набори даних, які дають змогу простежити в часі дію залежних від дослідника чинників, насамперед різних агрозаходів [2]. Завдяки цьому тривалі польові досліди мають великий потенціал щодо подальшого вдосконалення технологій вирощування сільськогосподарських рослин, розвитку систем землеробства, захисту довкілля в аграрній сфері [3 -7].…”